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Your search keyword '"Khan, Fakhri Alam"' showing total 49 results
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49 results on '"Khan, Fakhri Alam"'

1. WristSense: A Wrist-Wear Dataset for Identifying Aggressive Tendencies

Author

Almubairik, Norah Ahmed, Khan, Fakhri Alam, Mohammad, Rami Mustafa, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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9. Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study.

Author

Alotaibi, Basmah, Khan, Fakhri Alam, and Mahmood, Sajjad

Subjects
FEDERATED learning, SKEWNESS (Probability theory), DATA transmission systems, DIGITAL libraries, CLASSROOM environment, SUPERVISED learning
Abstract

Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client's limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Design and Performance Optimisation of the Hexaboards for CMS HGCAL On-Cassette Readout Electronics

Author

Khan, Fakhri Alam

Subjects
Detectors and Experimental Techniques
Abstract

A new high-granularity endcap calorimeter, HGCAL, is foreseen to be installed later this decade in the CMS experiment. We present the design and performance of the Hexaboard, a complex hexagonal multi-layer PCB equipped with multiple HGCROC ASICs to read out the signals from silicon pads with low noise and large dynamic range. The Hexaboards are glued to silicon sensors and connect to their pads via wire bonds through holes in the PCBs. The Hexaboard also connects to mezzanine boards for powering, trigger and data concentration and transfer. More than ten variants of the Hexa\-board are required to cover the circular fiducial area of the CMS endcaps. Detailed performance measurements, and comparative PCB simulations using Ansys SIwave, are presented.

Published
2022

21. Grid-Enabled Non-Invasive Blood Glucose Measurement

Author

Elsayed, Ibrahim, Han, Jianguo, Liu, Ting, Wöhrer, Alexander, Khan, Fakhri Alam, Brezany, Peter, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bubak, Marian, editor, van Albada, Geert Dick, editor, Dongarra, Jack, editor, and Sloot, Peter M. A., editor

Published
2008
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30. Convolutional Neural Network for Histopathological Osteosarcoma Image Classification.

Author

Ahmed, Imran, Sardar, Humaira, Aljuaid, Hanan, Khan, Fakhri Alam, Nawaz, Muhammad, and Awais, Adnan

Subjects
CONVOLUTIONAL neural networks, HISTOPATHOLOGY, OSTEOSARCOMA, COMPUTER-aided diagnosis, HIERARCHICAL Bayes model, BONE cancer
Abstract

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists' experience. Convolutional Neural Network (CNN--an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN's basic architecture to reduce overfitting. Experimental results demonstrate that oversamplingmight be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96%& 81% with a balanced dataset, respectively. The regularized CNNmodel training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset. [ABSTRACT FROM AUTHOR]

Published
2021
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